Image similarity determining device and method, and an image feature acquiring device and method

ABSTRACT

An image similarity determining device and method and an image feature acquiring device and method are provided. The image similarity determining device comprises a preprocessing unit for extracting feature points of each input image region of an input image and each image region to be matched of a data source image; a matched feature point set determining unit for determining one to one matched feature point pairs between input image regions and image regions to be matched to determine matched feature point sets; a geometry similarity determining unit for determining a geometry similarity between the input image region and the image region to be matched based on distribution of respective feature points in the matched feature point sets; and an image similarity determining unit for determining similarity between input image and data source image based on geometry similarities between input image regions and corresponding image regions to be matched.

CROSS REFERENCE TO RELATED APPLICATIONS

This claims the benefit of Chinese Application No. 201310237721.X, filedJun. 14, 2013, the disclosure of which is incorporated herein byreference.

FIELD OF THE INVENTION

The present disclosure relates to the field of image processing, and inparticular, to a device and method for determining the similaritybetween images, and a device and method for acquiring image features.

BACKGROUND OF THE INVENTION

In the field of image processing, the image matching (namely, how todetermine the similarity between images) is very important in therealization of image content-based retrieval application. The currentimage matching can be mainly divided into gray-based image matching andfeature-based image matching. The features processed by thefeature-based image matching typically include color feature, texturefeature, etc. However, the accuracy of the current image matching stillneeds to be improved.

SUMMARY OF THE INVENTION

A brief summary of the present disclosure is given below, so as toprovide a basic understanding on some aspects of the present disclosure.It will be appreciated that the summary is not an exhaustive descriptionof the present disclosure. It is not intended to define a key orimportant part of the present disclosure, nor is it intended to limitthe scope of the present disclosure. It aims to give some concepts in asimplified form, as a preface to the more detailed description describedlater.

In view of above drawbacks of the prior art, an object of the presentdisclosure is to provide an image similarity determining device andmethod and an image feature acquiring device and method, to overcome atleast the above problems existing in the prior art.

According to an aspect of the present disclosure, there is provided animage similarity determining device for determining similarity of aninput image and a data source image, the image similarity determiningdevice comprising: a preprocessing unit configured for dividing theinput image into at least one input image region and dividing the datasource image into at least one image region to be matched, andextracting feature points of each input image region and each imageregion to be matched; a matched feature point set determining unitconfigured for determining, with respect to each input image region, oneto one matched feature point pairs between the input image region andeach image region to be matched, according to the feature similaritiesbetween each feature point of the input image region and each featurepoint of the corresponding image regions to be matched, and forming amatched feature point set including all the matched feature points forthe input image region and its corresponding image regions to be matchedaccording to the one to one matched feature point pairs; a geometrysimilarity determining unit configured for determining, with respect toeach input image region and its corresponding image region to bematched, a geometry similarity between the input image region and thecorresponding image region to be matched, based on the distribution ofrespective feature points in the matched feature point set of the inputimage region and the distribution of respective feature points in thematched feature point set of the corresponding image region to bematched; and an image similarity determining unit configured fordetermining the image similarity between each input image region and itscorresponding image region to be matched, according to the geometrysimilarity between the input image region and the corresponding imageregion to be matched, and determining the similarity between the inputimage and the data source image according to the image similaritiesbetween each of the input image regions of the input image andrespective corresponding image regions to be matched.

According to another aspect of the present disclosure, there is providedan image feature acquiring device, comprising: a preprocessing unitconfigured for preprocessing an input image so as to divide the inputimage into at least one input image region and extract feature points ofeach input image region; a feature region constructing unit configuredfor constructing a feature region with respect to each feature point ina feature point set of each input image region having at least threefeature points, wherein the feature region satisfies with the followingconditions: the feature point is used as a vertex of the feature region;a ray formed by the vertex and at least one of the other feature pointsin the feature point set is used as an edge of the feature region, thefeature region constructed by two of said edges includes all featurepoints of the feature point set, and the angle of the feature region isthe smallest; and an image feature acquiring unit configured fordetermining geometric features of the input image region, according tothe distribution of respective feature points in each of the featureregions, as a type of image feature of the input image region.

According to still another aspect of the present disclosure, there isprovided An image similarity determining method for determiningsimilarity of an input image and a data source image, the imagesimilarity determining method comprising: dividing the input image intoat least one input image region and dividing the data source image intoat least one image region to be matched, and extracting feature pointsof each input image region and each image region to be matched;determining, with respect to each input image region, one to one matchedfeature point pairs between the input image region and each image regionto be matched, according to the feature similarities between eachfeature point of the input image region and each feature point ofrespective image regions to be matched, and forming a matched featurepoint set including all the one to one matched feature points for theinput image region and its corresponding image regions to be matchedaccording to the one to one matched feature point pairs; determining,with respect to each input image region and its corresponding imageregion to be matched, a geometry similarity between the input imageregion and the corresponding image region to be matched, based on thedistribution of respective feature points in the matched feature pointset of the input image region and the distribution of respective featurepoints in the matched feature point set of the corresponding imageregion to be matched; and determining the image similarity between eachinput image region and its corresponding image region to be matchedaccording to the geometry similarity between the input image region andthe corresponding image region to be matched, and determining thesimilarity between the input image and the data source image accordingto the image similarities between each of the input image regions of theinput region and respective corresponding image regions to be matched.

According to still another aspect of the present disclosure, there isprovided an image feature acquiring method, comprising: preprocessingthe input image so as to divide the input image into at least one inputimage region and extract feature points of each input image region, andforming a feature point set including all of the feature points withrespect to each of the input images; constructing a feature region withrespect to each feature point in a feature point set of each input imageregion having at least three feature points, wherein the feature regionsatisfies with the following conditions: the feature point is used as avertex of the feature region; a ray formed by the vertex and at leastone of the other feature point in the feature point set is used as anedge of the feature region, the feature region constructed by two ofsaid edges includes all feature points of the matched feature point set,and the angle of the feature region is the smallest; and determininggeometric features of the input image region, according to thedistribution of respective feature points in each of the feature region,as a type of image feature of the input image region.

According to still another aspect of the present disclosure, there isprovided an electronic apparatus which includes the image similaritydetermining device or the image feature acquiring device as describedabove.

According to still another aspect of the present disclosure, there isprovided a program by which the computer is used as the image similaritydetermining device or the image feature acquiring device as describedabove.

According to still another aspect of the present disclosure, there isprovided a corresponding computer-readable storage medium on which thecomputer program that can be executed by a computing apparatus isstored, when executed, the computer program enables said computingapparatus to perform the image similarity determining method or theimage feature acquiring method as described above.

The above mentioned image similarity determining device and methodaccording to embodiments of the present disclosure can at least improvethe accuracy of image matching, and the image feature acquiring deviceand method according to embodiments of the present disclosure can atleast provide a new image feature acquiring way for the image matching.

Through the following detailed description of the best mode of thepresent disclosure in conjunction with the accompanying drawings, theseand other advantages of the present disclosure will become moreapparent.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may be understood better by referring to descriptionprovided in conjunction with the accompanying drawings, wherein the sameor similar reference signs are used to represent the same or similarcomponents in all of the figures. The figures and the following detaileddescription are included in the specification and form a part of thespecification, and used to further explain preferred embodiments of thepresent disclosure and explain principle and object of the presentdisclosure by examples. Wherein:

FIG. 1 is a block diagram schematically illustrating an exemplarystructure of the image similarity determining device according to anembodiment of the present disclosure.

FIG. 2 illustrates a specific implementation of the geometry similaritydetermining unit shown in FIG. 1.

FIGS. 3 a and 3 b schematically illustrates two examples of the featureregion according to an embodiment of the present disclosure.

FIG. 4 illustrates a specific implementation of the geometry similaritydetermining subunit shown in FIG. 2.

FIG. 5 illustrates an exemplary structure block diagram of the imagefeature acquiring device according to an embodiment of the presentdisclosure.

FIG. 6 is a flowchart of an exemplary process of the image similaritydetermining method according to an embodiment of the present disclosure.

FIG. 7 is a flowchart of an exemplary process of the image featureacquiring method according to an embodiment of the present disclosure.

FIG. 8 shows a structure view of hardware configuration of a possibleinformation processing apparatus used to implement an image similaritydetermining device and method and an image feature acquiring device andmethod according to an embodiment of the present disclosure.

The skilled person should understand that elements in the figures areillustrated for simplicity and clarity, and are not necessarily drawn toscale. For example, the size of some elements in the accompanyingdrawings may be enlarged with respect to other elements, so as tofacilitate improving the understanding of embodiments of the presentdisclosure.

DETAILED DESCRIPTION OF THE INVENTION

Exemplary embodiments of the present disclosure are described below inconjunction with the accompanying drawings. For the sake of clarity andconciseness, not all the features of actual implementations aredescribed in the specification. However, it is to be appreciated thatduring developing any such actual implementations, numerousimplementation-specific decisions must be made to achieve thedeveloper's specific goals, for example, compliance with system-relatedand business-related constraints which will vary from one implementationto another. Moreover, it is also to be appreciated that such adevelopment effort might be very complex and time-consuming, but willnevertheless be a routine task for those skilled in the art having thebenefit of this disclosure.

It is further noted that only device structures and/or steps closelyrelevant to implementation of the present disclosure are illustrated inthe drawings while omitting other details less relevant to the presentdisclosure so as not to obscure the present disclosure due to thoseunnecessary details.

The image similarity determining device according to embodiments of thepresent disclosure is used to determine the similarity between the inputimage and the data source images.

According to embodiments of the present disclosure, for example, theinput image may be an image input from a user, an image taken by anelectronic apparatus, such as a mobile phone and a camera, or an imageacquired from Internet by a user. Further, the data source image may bean image from data sources, such as a variety of special databases,general databases, websites, etc., or an image from a combination ofthese data sources.

FIG. 1 is a block diagram schematically illustrating an exemplarystructure of the image similarity determining device according to anembodiment of the present disclosure.

As shown in FIG. 1, the image similarity determining device according toan embodiment of the present disclosure includes: a preprocessing unit11 configured for dividing the input image into at least one input imageregion and dividing the data source image into at least one image regionto be matched, and extracting feature points of each input image regionand each image region to be matched; a matched feature point setdetermining unit 13, configured for determining, with respect to eachinput image region, one to one matched feature point pairs between theinput image region and each of the image regions to be matched,according to the feature similarities between each feature point of theinput image region and each feature point of the corresponding imageregions to be matched, and forming a matched feature point set includingall the matched feature points for the input image region and itscorresponding image regions to be matched according to the one to onematched feature point pairs; a geometry similarity determining unit 15configured for determining, with respect to each input image region andits corresponding image region to be matched, a geometry similaritybetween the input image region and the corresponding image region to bematched, based on the distribution of respective feature points in thematched feature point set of the input image region and the distributionof respective feature points in the matched feature point set of thecorresponding image region to be matched; and an image similaritydetermining unit 17 configured for determining the image similaritybetween each input image region and its corresponding image region to bematched, according to the geometry similarity between the input imageregion and the corresponding image region to be matched, and determiningthe similarity between the input image and the data source imageaccording to image similarities between each of the input image regionsof the input image and respective corresponding image regions to bematched.

As shown in FIG. 1, the preprocessing unit 11 performs dividing processto the input image and respective data source images respectively. Forexample, according to features of the image, such as gray, edge,texture, structure, etc., the preprocessing unit 11 divides the inputimage into a plurality of input image regions, and divides each datasource image into a plurality of image regions as the image region to bematched. The input image and data source images can be divided byadopting existing image dividing techniques, or by adopting regionaldetection techniques, for example, the maximum stable extremal region(MSER) detection operator, which has advantages of high repeatabilityand high resolution. The above mentioned image dividing methods are wellknown in the art, will not be discussed in more detail herein.

When respective input image regions and respective regions to be matchedof the data source image are obtained, the preprocessing unit 11extracts feature points for each of the divided image areas. Accordingto embodiments of the present disclosure, for example, the preprocessingunit 11 can obtain location information, scale size, directioninformation of the feature point by using SIFT (Scale Invariant FeatureTransform) detection and extract the feature point of the image regionby using SIFT feature vector describing the feature point.

Referring to FIG. 1, when the feature points of each of the imageregions including the input image regions and the image regions to bematched were obtained by the preprocessing unit 11, the matched featurepoint set determining unit 13 determines, with respect to each inputimage region, the feature similarities between each feature point of theinput image region and each feature point of respective image regions tobe matched. For example, when m feature points are extracted from aninput image region Ra, and n feature points are extracted from an imageregion to be matched Rb, the feature similarities between each featurepoint {F}_(i=1) ^(m) feature points of the input image region Ra andeach feature point of n feature points of the image region to be matchedRb are determined. According to one embodiment of the presentdisclosure, the feature similarity between feature points can bedetermined from the distance between feature points. For example, withrespect to a pair of feature points consisted of a feature point in theinput image region and a feature point in the image region to bematched, the reciprocal of the Euclidean distance between the featuredescribed vectors of the pair of feature points can be calculated as thefeature similarity between the pair of feature points. The specificcalculation method for the distance of the feature points are well knownin the art, and is not discussed in more detail herein.

The matched feature point set determining unit 13 determines the one toone matched feature point pairs between the input image region and eachof the image regions to be matched based on the similarity betweenfeature points, and forming a matched feature point set including allthe one to one matched feature points respectively for the input imageregion and its corresponding image regions to be matched according tothe one to one matched featured point pairs.

According to embodiments of the present disclosure, the matched featurepoint set determining unit 13 is configured to construct a cost matrixbased on the feature similarity between respective feature points in theinput image region and respective feature points in the correspondingimage region to be matched, and determine the one to one matched featurepoint pairs between the input image region and the corresponding imageregions to be matched according to the cost matrix.

Specifically, the cost matrix is constructed by using the distancebetween the feature point pairs, and the one to one matched featurepoint pairs between the input image region and the image regions to bematched is determined by using an approach such as Hungarian algorithmand according to the minimum cost principle. According to preferredembodiments of the present disclosure, when the cost matrix isconstructed, the distance between the feature point pairs in which thedistance between feature points is larger than the first given threshold(namely, the feature point pairs being considered as not matched) can beset to, for example, ∞. And the cost matrix is constructed by using thedistance between the feature point pairs in which the distance betweenfeature points is less than the first given threshold (namely, thefeature point pairs having a relatively large feature similarity), sothat the subsequent calculation performed when the one to one matchedfeature point pair is determined by using the cost matrix is simplified.According to one embodiment of the present disclosure, the matchedfeature point set determining unit 13 can find a reasonable set of oneto one matched feature point pairs in the remained feature point pairsaccording to the minimum cost principle and for example, by using theHungarian algorithm, so as to form a matched feature point set includingall one to one matched feature points for the input image region and itscorresponding image region to be matched respectively.

It should be noted here that, for a certain input image region, if thedistances between each feature point in the input image region and afeature point in a certain image region to be matched are larger thanthe first given threshold, it can be considered that the input imageregion and the image region to be matched are not matched, accordingly,there is no need to perform subsequent process of determining the one toone matched feature point pairs for the cost matrix constructed by thedistances. Furthermore, in the present disclosure, the image region tobe matched corresponding to the input image region refers to the imageregion to be matched that has one to one matched feature point pairswith the input image region determined by the matched feature point setdetermining unit 13.

Although it is exemplified above that the cost matrix is constructedfrom the distance between the feature point pairs formed by the featurepoint in the input image region and the feature point in the imageregion to be matched, the present disclosure is not limited thereto. Forexample, the cost matrix can be constructed by taking the featuresimilarity between the feature point pairs calculated from the distancebetween the feature point pairs (such as the reciprocal of the distancebetween the feature point pairs) as entities, and the one to one matchedfeature points between the input image region and the image region to bematched can be determined from the cost matrix thus constructed.

Still taking the case in which m feature points are extracted from theinput image region R_(a) and n feature points are extracted from theimage region to be matched R_(b) as an example, for example, it isdetermined, from the cost matrix constructed from the feature similaritybetween respective feature point pairs, that there are i one to onematched feature point pairs between the input image region and the imageregion to be matched (accordingly, the image region to be matched is theone corresponding to the input image region), such as{P_(j′)Q_(j)}_(j=1) ^(i), wherein P_(j) is the matched feature point inthe input image region, and Q is the matched feature point in the imageregion to be matched. Thus, the matched feature point set P={P₁, P₂ . .. , P_(i)} including all matched feature points P_(i) is formed for theinput image region Ra and the matched feature point set Q={Q₁, Q₂, . . ., Q_(i)} including all matched feature points Q_(j) is formed for theimage region to be matched R_(b).

When the one to one matched feature point pairs are obtained withrespect to the input image region and the image region to be matched sothat the matched feature point sets are formed for the input imageregion and the corresponding image region to be matched, the geometrysimilarity determining unit 15 determines, with respect to each inputimage region and its corresponding image region to be matched, thegeometry similarity between the input image region and the correspondingimage region to be matched, based on the distribution of respectivefeature points in the matched feature point set of the input imageregion and the distribution of respective feature points in the matchedfeature point set of the corresponding image region to be matched.

The present disclosure provides a method for determining the geometrysimilarity between two regions by using the affine invariant matrixrepresenting the geometry relationship between the matched feature pointpairs of the input image region and the image region to be matched.

FIG. 2 illustrates a specific implementation of the geometry similaritydetermining unit 15 shown in FIG. 1.

As shown in FIG. 2, the geometry similarity determining unit 15includes: a feature region constructing subunit 151 and a geometrysimilarity determining subunit 153.

The feature region constructing subunit 151 is configured forrespectively constructing, with respect to each input image regionhaving at least three matched feature points included in the matchedfeature point set and its corresponding image region to be matched,feature regions for each feature point in the matched feature point setof the input image region and each feature point in the matched featurepoint set of the corresponding image region to be matched, wherein thefeature region satisfies with the following conditions: the featurepoint is used as a vertex of the feature region; a ray formed by thevertex and at least one of the other feature points in the correspondingmatched feature point set is used as an edge of the feature region, thefeature region constructed by two of said edges includes all featurepoints of the matched feature point set, and the angle of the featureregion is the smallest.

Still taking the case in which the matched feature point set of theinput image region R_(a) is P={P₁, P₂, . . . , P₁} and the matchedfeature point set of the corresponding image region to be matched R_(b)is Q={Q₁, Q₂, . . . , Q_(i)} as an example, the operation of the featureregion constructing subunit 151 is detailed.

The feature region satisfying the following conditions is searched withrespect to each feature point P_(j) in the matched feature point setP={P₁, P₂, . . . P_(i)} of the input image region R_(a):

1) the feature point P_(j) is used as a vertex of the feature region;

2) a ray formed by the vertex and at least one of the other featurepoints (except for the vertex) in the matched feature point set Pconstitutes an edge of the feature region;

3) the feature region constructed by two edges satisfying condition 2)include all feature points of the matched feature point set P;

4) the angle of the feature region is the smallest.

Similarly, the feature region satisfying the conditions similar to theabove conditions is searched with respect to each feature point Q_(j) inthe matched feature point set Q={Q₁, Q₂, . . . , Q_(i)} of the imageregion to be matched R_(b).

According to preferred embodiments of the present disclosure, thefeature region formed for the input image region and the image region tobe matched is a sector region. But the present disclosure is not limitedthereto, for example, a triangle region or an open region having andonly having the two edges as its edges (namely, the region doesn't havethe third edge) etc. is formed based on two edges satisfying condition2).

FIGS. 3 a and 3 b illustrate two examples of the feature region formedaccording to above conditions.

FIG. 3 a shows a sector feature region R constructed with respect to thematched feature point set P having seven matched feature point by takingP₁ as a vertex, wherein, the feature points P₂, P₄ and P₁ constitute oneedge of the feature region R, feature points P₃ and P₁ constituteanother edge of the feature region R, other feature points P₅, P₆ and P₇in the matched feature point set P fall in the feature region R. It canbe verified that the sector region shown in FIG. 3 a is the onesatisfying above conditions 1)-4). Similarly, a sector region satisfyingabove conditions 1)-4) can be constructed with respect to every otherfeature point in the matched feature point set P by taking the featurepoint as a vertex.

Similarly, a feature point set can be constructed respectively for eachfeature point in the matched feature point set Q of the image region tobe matched.

It should be noted here that, when the other feature points in thematched feature point set are distributed in a more dispersed mannercompared to the feature point as a vertex, the angle of the featureregion formed with respect to the feature point may be larger than 180degrees (see FIG. 3 b).

Returning to FIG. 2, when the feature region constructing subunit 151constructs a feature region for each of matched feature points in theinput image region and its corresponding image region to be matched, thegeometry similarity determining subunit 153 determines, with respect toeach input image region and its corresponding image region to bematched, a geometry similarity between the input image region and thecorresponding image region to be matched, based on the distribution offeature points in respective feature regions of the input image regionand the distribution of feature points in respective feature regions ofthe corresponding image region to be matched.

According to the present disclosure, it is proposed that the geometricrelation matrix is constructed based on the distribution of matchedfeature points in respective feature regions of the input image regionand the distribution of matched feature points in respective featureregions of the corresponding image region to be matched, and thegeometry similarity between the input image region and the image regionto be matched is determined according to the distance between geometricrelation matrixes.

FIG. 4 illustrates a specific implementation of the geometry similaritydetermining subunit 153 shown in FIG. 2.

As shown in FIG. 4, the geometry similarity determining subunit 153includes: a geometric relation matrix constructing module 1531 and ageometry similarity calculating module 1532.

The geometric relation matrix constructing module 1531 is configured toconstruct a geometric relation matrix for the input image region and thecorresponding image region to be matched respectively, based on thedistribution of feature points in respective feature regions of theinput image region and the distribution of feature points in respectivefeature regions of the corresponding image region to be matched.

According to one embodiment of the present disclosure, the geometricrelation matrix constructing module 1531 can be configured to construct,with respect to any one of the input image region and its correspondingimage region to be matched, the geometric relation matrix for the imageregion in a way such that each feature point in the matched featurepoint set of the image region corresponds to a row or a column of thegeometric relation matrix, and different values are assigned to eachentry in the row vector or column vector of the geometric relationmatrix corresponding to the feature point, according to whetherrespective feature points of the feature point set fall in the featureregion constructed for each feature point or fall on the edge of thefeature region.

For example, in the case that the input image region R_(a) and itscorresponding image region to be matched have four matched feature pointpairs (namely, as to the matched feature point set P1={P_(j)}_(j=1) ⁴the input image region R_(a) and the matched feature point setQ1={Qj}_(j=1) ⁴ of the image region to be matched R_(b)), with respectto the matched feature point set P₁={P_(j)}_(j×1) ⁴ of the input imageregion R_(a), the feature point P_(j) is assigned a value correspondingto the j-th row entry [h_(j1), h_(j2), h_(j3), h_(j4)] of the geometricrelation matrix HP_(4×4), for example, with respect to the featureregion formed by the vertex P_(j), the first row entries of thegeometric relation matrix may be assigned a value according to thefollowing rule:

If the feature point P₁ of the matched feature point set P falls on theray formed by the vertex P_(j) and other vertex of the matched featurepoint set (namely, an edge of the feature region), a value of 1 isassigned to h_(j1), otherwise, if the feature point P₁ falls in thefeature region constructed by the vertex P_(j), a value of 0 is assignedto h_(j1).

The above method for constructing a geometric relation matrix is just aexample, for example, when the feature point falls in the featureregion, a value of 1 is assigned to the matrix entry corresponding tothe feature point, and when the feature point falls on the edge of thefeature region, a value of 0 is assigned to the matrix entrycorresponding to the feature point.

With respect to other matched feature points P₂, P₃, P₄ in the matchedfeature point set P_(j) of the input image region, values are assignedto the j-th row elements h_(j2), h_(j3), h_(j4) of the geometricrelation matrix HP_(4×4) in similar manner, thus, values are assigned toeach relation matrix of the geometric relation matrix HP_(4×4), so as tocomplete the construction of the geometric relation matrix of the inputimage region.

As to the image region to be matched, the geometric relation matrixconstructing module 1531 can perform similar operations to the matchedfeature point set Q of the image region to be matched corresponding tothe input image region, so as to construct a geometric relation matrixHQ_(4×4) for the corresponding image region to be matched.

For example, the geometric relation matrix HP_(4×4) and the geometricrelation matrix HQ_(4×4) can be respectively constructed as thefollowing matrix based on the above assignment operation:

${HP}_{4 \times 4} = \begin{bmatrix}1 & 1 & 1 & 0 \\1 & 1 & 0 & 1 \\1 & 0 & 1 & 1 \\0 & 1 & 1 & 1\end{bmatrix}$ ${HQ}_{4 \times 4} = \begin{bmatrix}1 & 1 & 0 & 1 \\1 & 1 & 1 & 0 \\0 & 1 & 1 & 1 \\1 & 0 & 1 & 1\end{bmatrix}$

When the geometric relation matrix constructing module 1531 constructs ageometric relation matrix with respect to the input image region and itscorresponding image region to be matched, the geometry similaritycalculating module 1532 calculates, with respect to each input imageregion and its corresponding image region to be matched, a distancebetween the geometric relation matrix of the input image region and thegeometric relation matrix of the image region to be matched, as thegeometry similarity between the input image region and the correspondingimage region to be matched.

According to one embodiment of the present disclosure, the geometrysimilarity between the input image region and its corresponding imageregion to be matched can be calculated based on the geometric relationmatrices constructed for the two image regions.

For example, a XOR operation is performed on the two geometric relationmatrices, and the ratio between the XOR operation result and the valueof geometric relation matrix of the input image region is taken as thegeometry similarity between the input image region and the correspondingimage region to be matched.

For example, taking the case that the geometric relation matrix of theinput image region is the above mentioned HP_(4×4), and the geometricrelation matrix of the image region to be matched is the above mentionedHQ_(4×4) as an example, the geometry similarity between the input/imageregion and the corresponding image region to be matched can becalculated as XOR HP_(4×4), HQ_(4×4))/|P|=8/16=0.5.

According to embodiments of the present disclosure, with respect to eachinput image region, the geometry similarity between the input imageregion and its corresponding image region to be matched can bedetermined for the input image region and the corresponding image regionto be matched according to the distribution of feature points in theinput image region and the distribution of feature points in thecorresponding image region to be matched.

Although the above description of the geometry similarity determiningunit 15 just aims at the case of the input image region and onecorresponding image region to be matched, it will be appreciated by theskilled person that, in the case that there are a plurality of imageregions to be matched corresponding to the input image region, thegeometry similarity determining unit 15 calculates the geometrysimilarity with respect to the input image region and each correspondingimage region to be matched. Furthermore, in the case that the inputimage is divided into a plurality of input image regions, a processsimilar to that described with respect to above input image region R_(a)is performed on each input image region.

Returning to FIG. 1, when the geometry similarity determining unit 15determines the geometry similarity between the input image region andthe corresponding image region to be matched with respect to each inputimage region, the image similarity determining unit 17 determines theimage similarity between the input image region and the correspondingimage region to be matched according to the geometry similarity betweenthe input image region and the corresponding image region to be matched,and determines the similarity between the input image and the datasource image according to the image similarity between each input imageregion of the input image and respective corresponding image regions tobe matched.

According to one embodiment of the present disclosure, the imagesimilarity determining unit 17 can be configured to take the geometrysimilarity between the input image region and its corresponding imageregion to be matched as the image similarity between two image regions,and determine the similarity between the input image and the data sourceimage based on the image similarity between the input image region andrespective image region to be matched.

According to another embodiment of the present disclosure, the imagesimilarity determining unit 17 can be configured to determine the imagesimilarity between each input image region and its corresponding imageregion to be matched, according to the weighted combination of geometrysimilarity and feature similarity between the input image region and thecorresponding image region to be matched determined by the geometrysimilarity determining unit 15. Wherein, the feature similarity betweenthe input image region and the corresponding image region to be matchedcan be determined from the distance between the one to one matchedfeature point pairs of the two regions. For example, the featuresimilarity between the input image region and the corresponding imageregion to be matched can be determined from the feature similaritybetween the one to one matched feature point pairs determined when thematched feature point set determining unit 13 determines the matchedfeature point set. For another example, the image similarity determiningunit 17 may determine the feature similarity between each input imageregion and the corresponding image region to be matched from the featuresimilarity between feature points of the two image regions determined bythe preprocessing unit 11. The specific method for determining thefeature similarity is well known in the art, and is not discussed inmore detail herein.

Furthermore, according to one embodiment of the present disclosure, theimage similarity determining unit 17 is configured to take thecorresponding image region to be matched which has the largest imagesimilarity with each input image region of the input image, among aplurality of image region to be matched, as the matched image regionmatching the input image region, and determine the similarity betweenthe input image and the data source image according to the imagesimilarity between each input image region and the matched image region.

However, the present disclosure is not limited thereto, for example,when the image region to be matched having the largest image similaritywith the input image region is obtained, the image similaritydetermining unit 17 judges the image similarity of the image to bematched, if the largest image similarity is larger than the second giventhreshold, the image region to be matched is taken as the image regionto be matched of the input image region, otherwise, it can be consideredthat the input image region has no image region to be matched, so thatthe calculation amount of the image similarity determining unit isreduced.

When the matched image region is obtained, the mean value of the imagesimilarity between respective input image regions and the matched imageregion thereof may be taken as the image similarity between the inputimage and the data source image.

When the image similarities between the input image and a plurality ofdata source images are determined with respect to the input image byusing the image similarity determining device according to the presentdisclosure, for example, the image similarity determining device 1according to the present disclosure can sort the data source image indescending order based on the determined image similarity, so that theuser can obtain the data source image similar to the input image. Thepresent disclosure is particularly suitable for the image content-basedretrieval process.

Since the image similarity determining device and method take intoaccount the influence of the spatial distribution of feature points onthe image matching, it is possible to improve the accuracy of imagematching processing (namely, determining the similarity between images).

According to embodiments of the present disclosure, there is alsoprovided an image feature acquiring device that acquires thedistribution of the feature points in an image region as a type offeature of the image.

FIG. 5 illustrates an exemplary structure block diagram of the imagefeature acquiring device according to an embodiment of the presentdisclosure.

As shown in FIG. 5, the image feature acquiring device 2 includes: apreprocessing unit 21, a feature region constructing unit 23 and animage feature acquiring unit 25.

The preprocessing unit 21 is configured to preprocess the input image soas to divide the input image into at least one input image region andextracting feature points of each input image region. The configurationof the preprocessing unit 21 may be similar to, for example, thepreprocessing unit 11 described in conjunction with FIG. 1 in thepresent disclosure and can perform similar process, and will not bedescribed in detail herein.

The feature region constructing unit 23 is configured to construct afeature region for each feature point in a feature point set of eachinput image region having at least three feature points, wherein thefeature region satisfies with the following conditions: the featurepoint is used as a vertex of the feature region; a ray formed by thevertex and at least one of the other feature points in the correspondingfeature point set is used as an edge of the feature region, the featureregion constructed by two of said edges includes all the feature pointsof the feature point set, and the angle of the feature region is thesmallest. The configuration of the feature region constructing unit 23may be similar to, for example, the feature region constructing subunit151 described in conjunction with FIGS. 2 and 3 in the presentdisclosure and can perform similar process, and the detailed descriptionthereof is omitted here.

The image feature acquiring unit 25 is configured to determine geometricfeatures of the input image region, according to the distribution ofrespective feature points in each of the feature region, as a type ofimage feature of the input image region. For example, the geometrysimilarity determined by the geometry similarity determining subunit 151described in conjunction with FIGS. 2 and 4 can be taken as a type ofimage feature of the input image region.

According to an embodiment of the present disclosure, there is alsoprovided an image similarity determining method for determiningsimilarity between the input image and the data source image, anexemplary process of the image similarity determining method will bedescribed in conjunction with FIG. 6.

As shown in FIG. 6, the process flow 600 of the image similaritydetermining method according to an embodiment of the present disclosurebegins at S610, and then the process of S620 is performed.

In S620, the input image is divided into at least one input image regionand the data source image is divided into at least one image region tobe matched, and the feature points of each input image region and eachimage region to be matched are extracted. For example, S620 isimplemented by performing the process of the preprocessing unit 11described in conjunction with FIG. 1, and the description thereof isomitted here. Then, S630 is performed.

In S630, the one to one matched feature points between the input imageregion and each of the image regions to be matched are determinedaccording to the feature similarities between each feature point of theinput image region and each feature point of respective image regions tobe matched, and a matched feature point set including all the matchedfeature points is formed for the input image region and itscorresponding image regions to be matched according to the one to onematched feature points. For example, S630 is implemented by performingthe process of the matched feature point set determining unit 13described in conjunction with FIG. 1, and the description thereof isomitted here. Then, S640 is performed.

In S640, with respect to each input image region and its correspondingimage region to be matched, a geometry similarity between the inputimage region and the corresponding image region to be matched isdetermined based on the distribution of respective feature points in thematched feature point set of the input image region and the distributionof respective feature points in the matched feature point set of thecorresponding image region to be matched. For example, the step ofdetermining the geometry similarity can be implemented by performing theprocess of the geometry similarity determining unit 15 described inconjunction with FIGS. 2-4, and the description thereof is omitted here.

In S650, the image similarity between each input image region and itscorresponding image region to be matched is determined according to thegeometry similarity between the input image region and the correspondingimage region to be matched, and the image similarity between the inputimage and the data source image is determined according to the imagesimilarities between each of the input image regions of the input imageand respective corresponding image regions to be matched. Then, S660 isperformed. For example, S650 is implemented by performing the process ofthe image similarity determining unit 17 described in conjunction withFIG. 1, and the description thereof is omitted here.

According to an embodiment of the present disclosure, in S650, the imagesimilarity between each input image region and its corresponding imageregion to be matched can be determined according to the weightedcombination of geometry similarity and feature similarity between eachinput image region and its corresponding image region to be matched; andthe image similarity between the input image and the data source imageis determined by using the image similarities between the input imageregions and the image regions to be matched, determined from thegeometry similarity and the feature similarity.

The process flow 600 ends at S660.

Corresponding to the image feature acquiring device according toembodiments of the present disclosure, the present disclosure furtherprovides an image feature acquiring method, an exemplary process of theimage feature acquiring method will be described in conjunction withFIG. 7.

As shown in FIG. 7, the process flow 700 of the image feature acquiringmethod according to embodiments of the present disclosure begins atS710, and then the process of S720 is performed.

In S720, the input image is preprocessed so as to divide the input imageinto at least one input image region and extracting feature points ofeach input image region, and the feature point set including all featurepoints is formed with respect to each input image region. For example,S720 is implemented by performing the process of the preprocessing unit21 described in conjunction with FIG. 5, and the description thereof isomitted here. Then, S730 is performed.

In S730, a feature region is constructed for each feature point in afeature point set having at least three feature points, wherein thefeature region satisfies with the following conditions: the featurepoint is used as a vertex of the feature region; a ray formed by thevertex and at least one of the other feature point in the correspondingfeature point set is used as an edge of the feature region, the featureregion constructed by two of said edges include all the feature pointsof the feature point set, and the angle of the feature region is thesmallest. For example, S730 is implemented by performing the process ofthe feature region constructing unit 23 described in conjunction withFIG. 5, and the description thereof is omitted here. Then, S740 isperformed.

In S740, the geometric feature of the input image region is determinedas a type of image feature of the input image region according to thedistribution of respective feature points in each of the feature pointregion. For example, S740 is implemented by performing the process ofthe image feature acquiring unit 25 described in conjunction with FIG.5, and the description thereof is omitted here. Then, S750 is performed.

The process flow 700 ends at S750.

Compared with the prior art, there is provided (namely, the geometryfeature for representing the distribution of the feature points) adevice and method for acquiring a new image feature according toembodiments of the present disclosure, so that the accuracy of imagematching can be improved by using the new image feature in imagematching, for example (or, by using the combination of the new imagefeature and the traditional image feature for image matching). Further,the image similarity determining device and method according to thepresent disclosure can improve the accuracy of image matching.

In addition, the embodiments of the present disclosure provide anelectronic apparatus, the electronic apparatus is configured to includethe image similarity determining device 1 or the image feature acquiringdevice 2 described above. For example, the electronic apparatus may beany of the following devices: a mobile phone, a computer, a tablet, anda personal digital assistant etc. The electronic apparatus including theimage similarity determining device 1 or the image feature acquiringdevice 2 described above may be used for image searching based on imagecontent, for example. Correspondingly, the electronic apparatus can havethe beneficial effects and advantages as the image similaritydetermining device or the image feature acquiring device describedabove.

Respective component units, subunits in the above mentioned imagesimilarity determining device according to embodiments of the presentdisclosure can be configured by way of software, firmware, hardware, orany of combinations thereof. In the case of software or firmwareimplementation, programs constituting the software or firmware areinstalled to a machine with a dedicated hardware structure from astorage medium or a network, wherein the machine can execute variouscorresponding functions of the component units, subunits when beinginstalled various programs.

FIG. 8 shows a structure view of hardware configuration of a possibleinformation processing apparatus used to implement an image similaritydetermining device and method and an image feature acquiring device andmethod according to an embodiment of the present disclosure.

In FIG. 8, a central processing unit (CPU) 801 executes variousprocesses according to programs stored in a read only memory (ROM) 802or programs loaded from the storage section 808 to the random accessmemory (RAM) 803. In RAM 803, the data required when CPU 801 executesvarious processes is stored as necessary. CPU 801, ROM 802 and RAM 803are connected to each other via a bus 804. Input/output interface 805 isalso connected to the bus 804.

The following components are also connected to the input/outputinterface 805: an input section 806 (including a keyboard, mouse, etc.),the output section 807 (including a display, such as a cathode ray tube(CRT), liquid crystal display (LCD), etc. and a speaker, etc.), astorage section 808 (including hard disk, etc.), the communicationsection 809 (including a network interface card such as a LAN card,modem, etc.). The communication section 809 performs a communicationprocess via a network such as the Internet. If necessary, the drive 810can be connected to the input/output interface 805. Removable media 811such as a magnetic disk, optical disk, magneto-optical disk, asemiconductor memory or the like may be mounted on the drive 810 asrequired, such that a computer program read out therefrom may beinstalled into the storage section 808 as required.

In case of realizing the above described series of processing bysoftware, a program constituting the software is installed from anetwork such as the Internet or from a storage medium such as aremovable medium 811.

Those skilled in the art should understand that, the storage medium isnot limited to the removable storage medium 811 shown in FIG. 8 thatstores programs therein and is distributed in a separated form with theapparatus to provide a program to a user. Examples of the removablestorage medium 811 include a magnetic disk (including a floppy disk), anoptical disc (including a compact disc read-only memory (CD-ROM) and adigital versatile disk (DVD)), a magneto-optical disk (including a minidisk (MD) (Registration trademarks) and a semiconductor memory. Or, thestorage medium may be ROM 802, a hard disk contained in the storagesection 808, etc., which have programs stored therein and aredistributed to the user together with the apparatus including them.

The present disclosure also provides a program product in which machinereadable instruction codes are stored. The image similarity acquiringdevice and method according to embodiments of the present disclosure canbe executed when the instruction code is read and executed by themachine. Accordingly, various storage medium such as a magnetic disk,optical disk, magneto-optical disk, a semiconductor memory for carryingsuch a program product is also included in the present disclosure.

In the above description of the specific embodiments of the presentdisclosure, features that are described and/or illustrated with respectto one embodiment may be used in the same way or in a similar way in oneor more other embodiments and/or in combination with or instead of thefeatures of the other embodiments.

Furthermore, the methods according to the present disclosure shall notbe limited to being performed only in the chronological sequencedescribed in the specification but can also be performed in anotherchronological sequence, concurrently or separately. Therefore, thetechnical scope of the present disclosure will not be limited by thesequence in which the methods are performed as described in thespecification.

Additionally, it is obvious that each operational process of theaforementioned method according to the present disclosure can also berealized in the form of a computer-executable program stored in variousmachine-readable storage media.

In addition, the objects of the present invention can also be achievedby the way specified below: directly or indirectly supplying the storagemedium storing the aforementioned executable program code to a system oran apparatus, and reading and executing the program code by a computeror a central processing unit (CPU) in the system or apparatus.

In this case, as long as the system or the apparatus possesses thefunction to execute programs, embodiments of the present disclosure arenot restricted to the program, and the program may also assume any form,such as target program, interpreter-executed program, or script programsupplied to an operating system, etc.

The aforementioned machine-readable storage media include, but are notlimited to, various memories and storage units, semiconductor apparatus,magnetic units such as optical, magnetic and magneto-optical disks, aswell as other media adapted to storing information.

Moreover, embodiments of the present disclosure can also be realized byconnecting a customer information processing terminal to correspondingwebsites of the Internet, downloading and installing the computerprogram code according to the present disclosure into the informationprocessing terminal, and executing the program.

In summary, in embodiments according to the present disclosure, thepresent disclosure provides the following aspects, but not limitedthereto:

Aspect 1. An image similarity determining device for determiningsimilarity between an input image and a data source image, the imagesimilarity determining device comprising:

a preprocessing unit configured for dividing the input image into atleast one input image region and dividing the data source image into atleast one image region to be matched, and extracting feature points ofeach input image region and each image region to be matched;

a matched feature point set determining unit configured for determining,with respect to each input image region, one to one matched featurepoint pairs between the input image region and each image region to bematched, according to the feature similarities between each featurepoint of the input image region and each feature point of thecorresponding image regions to be matched, and forming a matched featurepoint set including all the matched feature points for the input imageregion and its corresponding image regions to be matched according tothe one to one matched feature point pairs;

a geometry similarity determining unit configured for determining, withrespect to each input image region and its corresponding image region tobe matched, a geometry similarity between the input image region and thecorresponding image region to be matched, based on the distribution ofrespective feature points in the matched feature point set of the inputimage region and the distribution of respective feature points in thematched feature point set of the corresponding image region to bematched; and

an image similarity determining unit configured for determining theimage similarity between each input image region and its correspondingimage region to be matched, according to the geometry similarity betweenthe input image region and the corresponding image region to be matched,and determining the similarity between the input image and the datasource image according to the image similarities between each of theinput image regions of the input image and respective correspondingimage regions to be matched.

Aspect 2. The image similarity determining device according to aspect 1,wherein the image similarity determining unit is configured fordetermining the image similarity between each input image region and itscorresponding image region to be matched, according to the weightedcombination of geometry similarity and feature similarity between theinput image region and the corresponding image region to be matched.

Aspect 3. The image similarity determining device according to aspect 1or 2, wherein the geometry similarity determining unit comprises:

a feature region constructing subunit configured for respectivelyconstructing, with respect to each input image region having at leastthree matched feature points included in the matched feature point setand its corresponding image region to be matched, feature regions foreach feature point in the matched feature point set of the input imageregion and each feature point in the matched feature point set of thecorresponding image region to be matched, wherein the feature regionsatisfies with the following conditions: the feature point is used as avertex of the feature region; a ray formed by the vertex and at leastone of the other feature points in the corresponding matched featurepoint set is used as an edge of the feature region, the feature regionconstructed by two of said edges includes all feature points of thematched feature point set, and the angle of the feature region is thesmallest;

a geometry similarity determining subunit configured for determining,with respect to each input image region and its corresponding imageregion to be matched, a geometry similarity between the input imageregion and the corresponding image region to be matched, based on thedistribution of feature points in respective feature regions of theinput image region and the distribution of feature points in respectivefeature regions of the corresponding image region to be matched.

Aspect 4. The image similarity determining device according to aspect 3,wherein the feature region is a sector region.

Aspect 5. The image similarity determining device according to aspect 3or 4, wherein the geometry similarity determining subunit furthercomprises:

a geometric relation matrix constructing module configured forconstructing a geometric relation matrix for the input image region andthe corresponding image region to be matched respectively, based on thedistribution of feature points in respective feature regions of theinput image region and the distribution of feature points in respectivefeature regions of the corresponding image region to be matched; and

a geometry similarity calculating module configured for calculating,with respect to each input image region and its corresponding imageregion to be matched, a distance between the geometric relation matrixof the input image region and the geometric relation matrix of the imageregion to be matched, as the geometry similarity between the input imageregion and the corresponding image region to be matched.

Aspect 6. The image similarity determining device according to aspect 5,wherein, the geometric relation matrix constructing module is configuredfor constructing, with respect to any of the input image region and itscorresponding image region to be matched, the geometric relation matrixfor the image region in a way such that each feature point in thematched feature point set of the image region corresponds to a row or acolumn of the geometric relation matrix, and different values areassigned to each element in the row vector or column vector of thegeometric relation matrix corresponding to the feature point accordingto whether respective feature point of the feature point set fallswithin the feature region constructed for each feature point or fall onthe edge of the feature region.

Aspect 7. The image similarity determining device according to any ofaspects 1-6, wherein the image similarity determining unit is configuredfor taking the corresponding image region to be matched which has thelargest similarity with each of the input image regions of the inputimage as a matched image region of the input image region, anddetermining the similarity between the input image and the data sourceimage according to the image similarities between each input imageregion and the matched image region thereof.

Aspect 8. The image similarity determining device according to any ofaspects 1-6, the matched feature point set determining unit isconfigured to construct a cost matrix based on the feature similaritybetween respective feature points in the input image region andrespective feature points in the corresponding image region to bematched, and determine the one to one matched feature points between theinput image region and the corresponding image regions to be matchedaccording to the cost matrix.

Aspect 9. An image feature acquiring device, comprising:

a preprocessing unit configured for preprocessing an input image so asto divide the input image into at least one input image region andextract feature points of each input image region;

a feature region constructing unit configured for constructing a featureregion with respect to each feature point in a feature point set of eachinput image region having at least three feature points, wherein thefeature region satisfies with the following conditions: the featurepoint is used as a vertex of the feature region; a ray formed by thevertex and at least one of the other feature points in the feature pointset is used as an edge of the feature region, the feature regionconstructed by two of said edges includes all feature points of thefeature point set, and the angle of the feature region is the smallest;and

-   -   an image feature acquiring unit configured for determining        geometric features of the input image region, according to the        distribution of respective feature points in each of the feature        regions, as a type of image feature of the input image region.

Aspect 10. The image similarity determining device according to aspect9, wherein the feature region is a sector region.

Aspect 11. An image similarity determining method for determiningsimilarity of an input image and a data source image, the imagesimilarity determining method comprising:

dividing the input image into at least one input image region anddividing the data source image into at least one image region to bematched, and extracting feature points of each input image region andeach image region to be matched;

determining, with respect to each input image region, one to one matchedfeature point pairs between the input image region and each image regionto be matched, according to the feature similarities between eachfeature point of the input image region and each feature point ofrespective image regions to be matched, and forming a matched featurepoint set including all the one to one matched feature points for theinput image region and its corresponding image regions to be matchedaccording to the one to one matched feature point pairs;

determining, with respect to each input image region and itscorresponding image region to be matched, a geometry similarity betweenthe input image region and the corresponding image region to be matched,based on the distribution of respective feature points in the matchedfeature point set of the input image region and the distribution ofrespective feature points in the matched feature point set of thecorresponding image region to be matched; and

determining the image similarity between each input image region and itscorresponding image region to be matched according to the geometrysimilarity between the input image region and the corresponding imageregion to be matched, and determining the similarity between the inputimage and the data source image according to the image similaritiesbetween each of the input image regions of the input region andrespective corresponding image regions to be matched.

Aspect 12. An image feature acquiring method, comprising:

preprocessing the input image so as to divide the input image into atleast one input image region and extract feature points of each inputimage region, and forming a feature point set including all of thefeature points with respect to each of the input images;

constructing a feature region with respect to each feature point in afeature point set of each input image region having at least threefeature points, wherein the feature region satisfies with the followingconditions: the feature point is used as a vertex of the feature region;a ray formed by the vertex and at least one of the other feature pointin the feature point set is used as an edge of the feature region, thefeature region constructed by two of said edges includes all featurepoints of the matched feature point set, and the angle of the featureregion is the smallest; and

-   -   determining geometric features of the input image region,        according to the distribution of respective feature points in        each of the feature region, as a type of image feature of the        input image region.

Aspect 13. An electric apparatus, including the image similaritydetermining device according to any of aspects 1-8 or the image featureacquiring device according to aspect 9 or 10.

Aspect 14. The electric apparatus according to aspect 13, wherein theelectric apparatus is a mobile phone, computer, tablet, or personaldigital assistant.

Aspect 15. A program by which a computer is used as the image similaritydetermining device according to any of aspects 1-8 or the image featureacquiring device according to aspect 9 or 10.

Aspect 16. A computer-readable storage medium on which the computerprogram that can be executed by a computing apparatus is stored, whenexecuted, the computer program enable said computing apparatus toperform the image similarity determining method according to aspect 11or the image feature acquiring method according to aspect 12.

Finally, as should be further explained, such relational terms as leftand right, first and second, etc., when used in the present disclosure,are merely used to differentiate one entity or operation from anotherentity or operation, without necessarily requiring or suggesting thatthese entities or operations have therebetween any such actual relationor sequence. Moreover, terms ‘comprise’, ‘include’ or other variants andany variants thereof are meant to cover nonexclusive inclusion, so thatprocesses, methods, objects or devices that include a series of elementsnot only include these elements, but also include other elements notexplicitly listed, or further include elements inherent in theprocesses, methods, objects or devices. Without more restrictions, anelement defined by the sentence ‘including a . . . ’ does not precludethe further inclusion of other identical elements in the processes,methods, objects or devices that include this element.

Although the present disclosure has been disclosed above by thedescription of specific embodiments of the present disclosure, it willbe appreciated that those skilled in the art can design variousmodifications, improvements and equivalents of the present disclosurewithin the spirit and scope of appended claims. Such modifications,improvements and equivalents should also be regarded as being covered bythe protection scope of the present disclosure.

1. An image similarity determining device for determining similaritybetween an input image and a data source image, the image similaritydetermining device comprising: a preprocessing unit configured fordividing the input image into at least one input image region anddividing the data source image into at least one image region to bematched, and extracting feature points of each input image region andeach image region to be matched; a matched feature point set determiningunit configured for determining, with respect to each input imageregion, one to one matched feature point pairs between the input imageregion and each image region to be matched, according to the featuresimilarities between each feature point of the input image region andeach feature point of respective image regions to be matched, andforming a matched feature point set including all the matched featurepoints for the input image region and its corresponding image regions tobe matched according to the one to one matched feature point pairs; ageometry similarity determining unit configured for determining, withrespect to each input image region and its corresponding image region tobe matched, a geometry similarity between the input image region and thecorresponding image region to be matched, based on the distribution ofrespective feature points in the matched feature point set of the inputimage region and the distribution of respective feature points in thematched feature point set of the corresponding image region to bematched; and an image similarity determining unit configured fordetermining the image similarity between each input image region and itscorresponding image region to be matched, according to the geometrysimilarity between the input image region and the corresponding imageregion to be matched, and determining the similarity between the inputimage and the data source image according to the image similaritiesbetween each of the input image regions of the input image region andrespective corresponding image regions to be matched.
 2. The imagesimilarity determining device according to claim 1, wherein the imagesimilarity determining unit is configured for determining the imagesimilarity between each input image region and its corresponding imageregion to be matched, according to the weighted combination of geometrysimilarity and feature similarity between the input image region and thecorresponding image region to be matched.
 3. The image similaritydetermining device according to claim 1, wherein the geometry similaritydetermining unit comprises: a feature region constructing subunitconfigured for respectively constructing, with respect to each inputimage region having at least three matched feature points included inthe matched feature point set and its corresponding image region to bematched, feature regions for each feature point in the matched featurepoint set of the input image region and each feature point in thematched feature point set of the corresponding image region to bematched, wherein the feature region satisfies with the followingconditions: the feature point is used as a vertex of the feature region;a ray formed by the vertex and at least one of the other feature pointsin the corresponding matched feature point set is used as an edge of thefeature region, the feature region constructed by two of said edgesincludes all feature points of the matched feature point set, and theangle of the feature region is the smallest; a geometry similaritydetermining subunit configured for determining, with respect to eachinput image region and its corresponding image region to be matched, ageometry similarity between the input image region and the correspondingimage region to be matched, based on the distribution of feature pointsin respective feature regions of the input image region and thedistribution of feature points in respective feature regions of thecorresponding image region to be matched.
 4. The image similaritydetermining device according to claim 3, wherein the feature region is asector region.
 5. The image similarity determining device according toclaim 3, wherein the geometry similarity determining subunit furthercomprises: a geometric relation matrix constructing module configuredfor constructing a geometric relation matrix for the input image regionand the corresponding image region to be matched respectively, based onthe distribution of feature points in respective feature regions of theinput image region and the distribution of feature points in respectivefeature regions of the corresponding image region to be matched; and ageometry similarity calculating module configured for calculating, withrespect to each input image region and its corresponding image region tobe matched, a distance between the geometric relation matrix of theinput image region and the geometric relation matrix of the image regionto be matched, as the geometry similarity between the input image regionand the corresponding image region to be matched.
 6. The imagesimilarity determining device according to claim 5, wherein, thegeometric relation matrix constructing module is configured forconstructing, with respect to any of the input image region and itscorresponding image region to be matched, the geometric relation matrixfor the image region in a way such that each feature point in thematched feature point set of the image region corresponds to a row or acolumn of the geometric relation matrix, and different values areassigned to each element in the row vector or column vector of thegeometric relation matrix corresponding to the feature point accordingto whether respective feature point of the feature point set fallswithin the feature region constructed for each feature point or fall onthe edge of the feature region.
 7. The image similarity determiningdevice according to claim 1, wherein the image similarity determiningunit is configured for taking the corresponding image region to bematched which has the largest similarity with each of the input imageregions of the input image as a matched image region of the input imageregion, and determining the similarity between the input image and thedata source image according to the image similarities between each inputimage region and the matched image region thereof.
 8. The imagesimilarity determining device according to claim 1, wherein, the matchedfeature point set determining unit is configured to construct a costmatrix based on the feature similarity between respective feature pointsin the input image region and respective feature points in thecorresponding image region to be matched, and determine the one to onematched feature point pairs between the input image region and thecorresponding image regions to be matched according to the cost matrix.9. An image feature acquiring device, comprising: a preprocessing unitconfigured for preprocessing an input image so as to divide the inputimage into at least one input image region and extract feature points ofeach input image region; a feature region constructing unit configuredfor constructing a feature region with respect to each feature point ina feature point set of each input image region having at least threefeature points, wherein the feature region satisfies with the followingconditions: the feature point is used as a vertex of the feature region;a ray formed by the vertex and at least one of the other feature pointsin the feature point set is used as an edge of the feature region, thefeature region constructed by two of said edges includes all featurepoints of the feature point set, and the angle of the feature region isthe smallest; and an image feature acquiring unit configured fordetermining geometric features of the input image region, according tothe distribution of respective feature points in each of the featureregions, as a type of image feature of the input image region.
 10. Theimage feature acquiring device according to claim 9, wherein the featureregion is a sector region.
 11. An image similarity determining methodfor determining similarity between an input image and a data sourceimage, the image similarity determining method comprising: dividing theinput image into at least one input image region and dividing the datasource image into at least one image region to be matched, andextracting feature points of each input image region and each imageregion to be matched; determining, with respect to each input imageregion, one to one matched feature point pairs between the input imageregion and each image region to be matched, according to the featuresimilarities between each feature point of the input image region andeach feature point of respective image regions to be matched, andforming a matched feature point set including all the one to one matchedfeature points for the input image region and its corresponding imageregions to be matched according to the one to one matched feature pointpairs; determining, with respect to each input image region and itscorresponding image region to be matched, a geometry similarity betweenthe input image region and the corresponding image region to be matched,based on the distribution of respective feature points in the matchedfeature point set of the input image region and the distribution ofrespective feature points in the matched feature point set of thecorresponding image region to be matched; and determining the imagesimilarity between each input image region and its corresponding imageregion to be matched according to the geometry similarity between theinput image region and the corresponding image region to be matched, anddetermining the similarity between the input image and the data sourceimage according to the image similarities between each of the inputimage regions of the input region and respective corresponding imageregions to be matched.